SoroushVahidi/scidocs huggingface dataset:
Managing Employee Compliance with Information Security Policies: The Critical Role of Top Management and Organizational Culture
(2012)

We develop an individual behavioral model that integrates the role of top management and organizational culture into the theory of planned behavior in an attempt to better understand how top management can influence security compliance behavior of employees. Using survey data and structural equation modeling, we test hypotheses on the relationships among top management participation, organizational culture, and key determinants of employee compliance with information security policies. We find that top management participation in information security initiatives has significant direct and indirect influences on employees’ attitudes towards, subjective norm of, and perceived behavioral control over compliance with information security policies. We also find that the top management participation strongly influences organizational culture which in turn impacts employees’ attitudes towards and perceived behavioral control over compliance with information security policies. Furthermore, we find that the effects of top management participation and organizational culture on employee behavioral intentions are fully mediated by employee cognitive beliefs about compliance with information security policies. Our findings extend information security research literature by showing how top management can play a proactive role in shaping employee compliance behavior in addition to the deterrence oriented remedies advocated in the extant literature. Our findings also refine the theories about the role of organizational culture in shaping employee compliance behavior. Significant theoretical and practical implications of ∗This project was partially funded by a grant to the authors from the Defense Information Systems Agency (DISA) of the Department of Defense (DoD). The authors express their thanks to the editor, senior editor, associate editor, and two anonymous reviewers for their detailed and constructive comments and suggestions throughout the review process. †Corresponding author.
A Social Network-Based Recommender System (SNRS)
(2010)

Social influence plays an important role in product marketing. However, it has rarely been considered in traditional recommender systems. In this paper we present a new paradigm of recommender systems which can utilize information in social networks, including user preferences, an item's general acceptance, and influence from social friends. A probabilistic model is developed to make personalized recommendations from such information. We extract data from a real online social network, and our analysis of this large dataset reveals that friends have a tendency to select the same items and give similar ratings. Experimental results on this dataset show that our proposed system not only improves the prediction accuracy of recommender systems but also remedies the data sparsity and cold-start issues inherent in collaborative filtering. Furthermore, we propose to improve the performance of our system by applying semantic filtering of social networks, and validate its improvement via a class project experiment. In this experiment we demonstrate how relevant friends can be selected for inference based on the semantics of friend relationships and finer-grained user ratings. Such technologies can be deployed by most
A study of the uniqueness of source code
(2010)

This paper presents the results of the first study of the uniqueness of source code. We define the uniqueness of a unit of source code with respect to the entire body of written software, which we approximate with a corpus of 420 million lines of source code. Our high-level methodology consists of examining a collection of 6,000 software projects and measuring the degree to which each project can be `assembled' solely from portions of this corpus, thus providing a precise measure of `uniqueness' that we call syntactic redundancy. We parameterized our study over a variety of variables, the most important of which being the level of granularity at which we view source code. Our suite of experiments together consumed approximately four months of CPU time, providing quantitative answers to the following questions: at what levels of granularity is software unique, and at a given level of granularity, how unique is software? While we believe these questions to be of intrinsic interest, we discuss possible applications to genetic programming and developer productivity tools.
Parameterized Pattern Matching: Algorithms and Applications
(1996)

The problem of finding sections of code that either are identical or are related by the systematic renaming of variables or constants can be modeled in terms of parameterized strings ( p-strings) and parameterized matches ( p-matches). P-strings are strings over two alphabets, one of which represents parameters. Two p-strings are a parameterized match ( p-match) if one p-string is obtained by renaming the parameters of the other by a one-to-one function. In this paper, we investigate parameterized pattern matching via parameterized suffix trees (p-suffix trees). We give two algorithms for constructing p-suffix trees: one (eager) that runs in linear time for fixed alphabets, and another that uses auxiliary data structures and runs in O(n log(n)) time for variable alphabets, where n is input length. We show that using a p-suffix tree for a pattern p-string P, it is possible to search for all p-matches of P within a text p-string T in space linear in |P| and time linear in |T | for fixed alphabets, or O( |T | log(min( |P|, _)) time and O( |P|) space for variable alphabets, where _ is the sum of the alphabet sizes. The simpler p-suffix tree construction algorithm eager has been implemented, and experiments show it to be practical. Since it runs faster than predicted by the above worst-case bound, we reanalyze the algorithm and show that eager runs in time O(min(t |S |+m(t, S) | t>0) log _)), where for an input p-string S, m(t, S) is the number of maximal p-matches of length at least t that occur within S, and _ is the sum of the alphabet sizes. Experiments with the author's program dup (B. Baker, in ``Comput. Sci. Statist.,'' Vol. 24, 1992) for finding all maximal p-matches within a p-string have found m(t, S) to be less than |S | in practice unless t is small. ] 1996 Academic Press, Inc.
Speech and Language Processing
(2006)

CHAPTER 18 Lexicons for Sentiment and Affect Extraction " [W]e write, not with the fingers, but with the whole person. The nerve which controls the pen winds itself about every fibre of our being, threads the heart, pierces the liver. " Virginia Woolf, Orlando " She runs the gamut of emotions from A to B. " Dorothy Parker, reviewing Hepburn's performance in Little Women In this chapter we turn to tools for interpreting affective meaning, extending our affective study of sentiment analysis in Chapter 7. We use the word 'affective', following the tradition in affective computing (Picard, 1995) to mean emotion, sentiment, personality , mood, and attitudes. Affective meaning is closely related to subjectivity, subjectivity the study of a speaker or writer's evaluations, opinions, emotions, and speculations (Wiebe et al., 1999). How should affective meaning be defined? One influential typology of affec-tive states comes from Scherer (2000), who defines each class of affective states by factors like its cognition realization and time course: Emotion: Relatively brief episode of response to the evaluation of an external or internal event as being of major significance. Mood: Diffuse affect state, most pronounced as change in subjective feeling, of low intensity but relatively long duration, often without apparent cause. Interpersonal stance: Affective stance taken toward another person in a specific interaction, colouring the interpersonal exchange in that situation. Figure 18.1 The Scherer typology of affective states, with descriptions from Scherer (2000).
Programming with a Differentiable Forth Interpreter
(2017)

Given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. In this paper, we consider the case of prior procedural knowledge for neural networks, such as knowing how a program should traverse a sequence, but not what local actions should be performed at each step. To this end, we present an end-to-end differentiable interpreter for the programming language Forth which enables programmers to write program sketches with slots that can be filled with behaviour trained from program input-output data. We can optimise this behaviour directly through gradient descent techniques on user-specified objectives, and also integrate the program into any larger neural computation graph. We show empirically that our interpreter is able to effectively leverage different levels of prior program structure and learn complex behaviours such as sequence sorting and addition. When connected to outputs of an LSTM and trained jointly, our interpreter achieves state-of-the-art accuracy for end-to-end reasoning about quantities expressed in natural language stories.
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets
(2015)

Despite the recent achievements in machine learning, we are still very far from achieving real artificial intelligence. In this paper, we discuss the limitations of standard deep learning approaches and show that some of these limitations can be overcome by learning how to grow the complexity of a model in a structured way. Specifically, we study the simplest sequence prediction problems that are beyond the scope of what is learnable with standard recurrent networks, algorithmically generated sequences which can only be learned by models which have the capacity to count and to memorize sequences. We show that some basic algorithms can be learned from sequential data using a recurrent network associated with a trainable memory.
TOLDI: An effective and robust approach for 3D local shape description
(2017)

Feature description for the 3D local shape in the presence of noise, varying mesh resolutions, clutter and occlusion is a quite challenging task in 3D computer vision. This paper tackles the problem by proposing a new local reference frame (LRF) together with a novel triple orthogonal local depth images (TOLDI) representation, forming the TOLDI method for local shape description. Compared with previous methods, TOLDI manages to perform efficient, distinctive and robust description for the 3D local surface simultaneously under various feature matching contexts. The proposed LRF differs from many prior ones in its calculation of the z-axis and x-axis, the z-axis is calculated using the normal of the keypoint and the x-axis is computed by aggregating the weighted projection vectors of the radius neighbors. TOLDI feature descriptors are then obtained by concatenating three local depth images (LDI) captured from three orthogonal view planes in the LRF into feature vectors. The performance of our TOLDI approach is rigorously evaluated on several public datasets, which contain three major surface matching scenarios, namely shape retrieval, object recognition and 3D registration. Experimental results and comparisons with the state-of-the-arts validate the effectiveness, robustness, high efficiency, and overall superiority of our method. Our method is also applied to aligning 3D object ∗Corresponding author Email addresses: jqyang@hust.edu.cn (Jiaqi Yang), hangfanzq@163.com (Qian Zhang), Yang Xiao@hust.edu.cn (Yang Xiao), zgcao@hust.edu.cn (Zhiguo Cao) Preprint submitted to Journal of LTEX Templates November 23, 2016 and indoor scene point clouds obtained by different devices (i.e., LiDAR and Kinect), the accurate outcomes further confirm the effectiveness of our method.
Synaptic patterning and the timescales of cortical dynamics
(2017)

Neocortical circuits, as large heterogeneous recurrent networks, can potentially operate and process signals at multiple timescales, but appear to be differentially tuned to operate within certain temporal receptive windows. The modular and hierarchical organization of this selectivity mirrors anatomical and physiological relations throughout the cortex and is likely determined by the regional electrochemical composition. Being consistently patterned and actively regulated, the expression of molecules involved in synaptic transmission constitutes the most significant source of laminar and regional variability. Due to their complex kinetics and adaptability, synapses form a natural primary candidate underlying this regional temporal selectivity. The ability of cortical networks to reflect the temporal structure of the sensory environment can thus be regulated by evolutionary and experience-dependent processes.
Consistent Generative Query Networks
(2018)

Stochastic video prediction is usually framed as an extrapolation problem where the goal is to sample a sequence of consecutive future image frames conditioned on a sequence of observed past frames. For the most part, algorithms for this task generate future video frames sequentially in an autoregressive fashion, which is slow and requires the input and output to be consecutive. We introduce a model that overcomes these drawbacks – it learns to generate a global latent representation from an arbitrary set of frames within a video. This representation can then be used to simultaneously and efficiently sample any number of temporally consistent frames at arbitrary time-points in the video. We apply our model to synthetic video prediction tasks and achieve results that are comparable to state-of-the-art video prediction models. In addition, we demonstrate the flexibility of our model by applying it to 3D scene reconstruction where we condition on location instead of time. To the best of our knowledge, our model is the first to provide flexible and coherent prediction on stochastic video datasets, as well as consistent 3D scene samples. Please check the project website https://bit.ly/2jX7Vyu to view scene reconstructions and videos produced by our model.
Single-label and multi-label conceptor classifiers in pre-trained neural networks
(2018)

Training large neural network models from scratch is not feasible due to over-fitting on small datasets and too much time consumed on large datasets. To address this, transfer learning, namely utilizing the feature extracting capacity learned by large models, becomes a hot spot in neural network community. At the classifying stage of pre-trained neural network model, either a linear SVM classifier or a Softmax classifier is employed and that is the only trained part of the whole model. In this paper, inspired by transfer learning, we propose a classifier based on conceptors called Multi-label Conceptor Classifier (MCC) to deal with multi-label classification in pre-trained neural networks. When no multi-label sample exists, MCC equates to Fast Conceptor Classifier, a fast single-label classifier proposed in our previous work, thus being applicable to single-label classification. Moreover, by introducing a random search algorithm, we further improve the performance of MCC on single-label datasets Caltech-101 and Caltech-256, where it achieves state-of-the-art results. Also, its evaluations with pre-trained rather than fine-tuning neural networks are investigated on multi-label dataset PASCAL VOC-2007, where it achieves comparable results.
Exploiting the web of data in model-based recommender systems
(2012)

The availability of a huge amount of interconnected data in the so called Web of Data (WoD) paves the way to a new generation of applications able to exploit the information encoded in it. In this paper we present a model-based recommender system leveraging the datasets publicly available in the Linked Open Data (LOD) cloud as DBpedia and LinkedMDB. The proposed approach adapts support vector machine (SVM) to deal with RDF triples. We tested our system and showed its effectiveness by a comparison with different recommender systems techniques -- both content-based and collaborative filtering ones.
Figurative language processing after traumatic brain injury in adults: A preliminary study
(2010)

Figurative speech (e.g., proverb, irony, metaphor, and idiom) has been reported to be particularly sensitive to measurement of abstract thinking in patients who suffer from impaired abstraction and language abilities. Metaphor processing was investigated with fMRI in adults with moderate to severe post-acute traumatic brain injury (TBI) and healthy age-matched controls using a valence-judgment task. We hypothesized that TBI patients would display decreased activation of the left inferior frontal gyrus (LIFG), which is considered central to semantic memory retrieval and abstract thought, in comparison with healthy controls. We also predicted that decreased activation in TBI individuals would correlate with their behavioral response times. A whole-brain analysis across the two participant groups revealed that patients did not strongly engage frontal and temporal regions related to semantic processing for novel metaphor comprehension, whereas control participants exhibited more intensive and concentrated activation within frontal and temporal areas. A region of interest (ROI) analysis verified that the LIFG was underactivated in TBI patients compared to controls across all conditions. TBI patients' impaired abstraction of novel stimuli may stem from reduced prefrontal control of semantic memory as well as disrupted interconnectivity of prefrontal cortex with other regions.
High-density 80 K SNP array is a powerful tool for genotyping G. hirsutum accessions and genome analysis
(2017)

High-throughput genotyping platforms play important roles in plant genomic studies. Cotton (Gossypium spp.) is the world’s important natural textile fiber and oil crop. Upland cotton accounts for more than 90% of the world’s cotton production, however, modern upland cotton cultivars have narrow genetic diversity. The amounts of genomic sequencing and re-sequencing data released make it possible to develop a high-quality single nucleotide polymorphism (SNP) array for intraspecific genotyping detection in cotton. Here we report a high-throughput CottonSNP80K array and its utilization in genotyping detection in different cotton accessions. 82,259 SNP markers were selected from the re-sequencing data of 100 cotton cultivars and used to produce the array on the Illumina Infinium platform. 77,774 SNP loci (94.55%) were successfully synthesized on the array. Of them, 77,252 (99.33%) had call rates of >95% in 352 cotton accessions and 59,502 (76.51%) were polymorphic loci. Application tests using 22 cotton accessions with parent/F1 combinations or with similar genetic backgrounds showed that CottonSNP80K array had high genotyping accuracy, good repeatability, and wide applicability. Phylogenetic analysis of 312 cotton cultivars and landraces with wide geographical distribution showed that they could be classified into ten groups, irrelevant of their origins. We found that the different landraces were clustered in different subgroups, indicating that these landraces were major contributors to the development of different breeding populations of modern G. hirsutum cultivars in China. We integrated a total of 54,588 SNPs (MAFs >0.05) associated with 10 salt stress traits into 288 G. hirsutum accessions for genome-wide association studies (GWAS), and eight significant SNPs associated with three salt stress traits were detected. We developed CottonSNP80K array with high polymorphism to distinguish upland cotton accessions. Diverse application tests indicated that the CottonSNP80K play important roles in germplasm genotyping, variety verification, functional genomics studies, and molecular breeding in cotton.
Comparison of different designs of cylindrical printed quadrifilar Helix antennas
(2012)

INMARSAT allocated frequencies are 1.525 GHz to 1.559 GHz as downlink and 1.625 GHz to 1.6605 GHz as uplink GPS allocated frequencies 1.57542 GHz in L1 band. In this paper we design two types of Helix antenna as PQHA to operate in allocated frequencies for INMARSAT and GPS application. We used for their optimum design Electromagnetic simulations and were verified by fabrication and VSWR measurement.
Normalization at the field level: Fractional counting of citations
(2010)

Van Raan et al. (2010) accepted our critique for the case of journal normalization (previously CPP/JCSm); CWTS has in the meantime adapted its procedures. However, a new indicator was proposed for field normalization (previously CPP/FCSm), called the “mean normalized citation score” (MNCS; cf. Lundberg, 2007). In our opinion, this latter change does not sufficiently resolve the problems. Since the new indicator is considered another “crown indicator,” it seems urgent to warn against and elaborate on these remaining problems. In addition to damaging evaluation processes at the level of individuals and institutions, the “crown indicator” is also used by CWTS for the “Leiden Rankings,” and flaws in it can therefore misguide policies at national levels. also
Caesar: A content router for high-speed forwarding on content names
(2012)

Today, high-end routers forward hundreds of millions of packets per second by means of longest prefix match on forwarding tables with less than a million IP prefixes. Information-Centric Networking, a novel form of networking where content is requested by its name, poses a new challenge in the design of high-end routers: process at least the same amount of packets, assuming a forwarding table that contains hundreds of millions of content prefixes. In this work we design and preliminarily evaluate Caesar, the first content router that supports name-based forwarding at high speed. Caesar efficiently uses available processing and memory units in a high-end router to support forwarding tables containing a billion content prefixes with unlimited characters.
Ultra wideband antennas: past and present
(2010)

Since the release by the Federal Communications Commission (FCC) of a bandwidth of 7.5GHz (from 3.1GHz to 10.6GHz) for ultra wideband (UWB) wireless communications, UWB is rapidly advancing as a high data rate wireless communication technology. As is the case in conventional wireless communication systems, an antenna also plays a very crucial role in UWB systems. However, there are more challenges in designing a UWB antenna than a narrow band one. A suitable UWB antenna should be capable of operating over an ultra wide bandwidth as allocated by the FCC. At the same time, satisfactory radiation properties over the entire frequency range are also necessary. This paper focuses on UWB planar printed circuit board (PCB) antenna design and analysis. Studies have been undertaken covering the areas of UWB fundamentals and antenna theory. Extensive investigations were also carried out on the development of UWB antennas from the past to present. First, the planar PCB antenna designs for UWB system is introduced and described. Next, the special design considerations for UWB antennas are summarized. State-of-the-art UWB antennas are also reviewed. Finally, a new concept for the design of a UWB antenna with a bandwidth ranging from 2GHz-11.3GHz is introduced, which satisfies the system requirements for S-DMB, WiBro, WLAN, CMMB and the entire UWB . KeywordsUltra wideband, planar PCB Antenna and bandwidth.